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dc.contributor.authorKinaman, Trevor
dc.contributor.authorBaehre, Andrew
dc.contributor.authorGray, Colin
dc.date.accessioned2016-05-11T03:07:23Z
dc.date.available2016-05-11T03:07:23Z
dc.date.issued2016-05-05
dc.identifier.urihttp://hdl.handle.net/10919/70956
dc.descriptionContains a Word document and PDF of the final report, and the Powerpoint and PDF for our final presentation.en_US
dc.description.abstractOur team is working with the Social Interactome team to assist in coding the recommender functionality for the Social Interactome network. That is supported by a website and system (modified version of Friendica) designed by the Social Interactome team to help recovering addicts. The team used Python to parse participant’s answers to survey questions, and applied an algorithm to that data to show each participant's most favorable friend matches. The team is working in concert with Prashant Chandrasekar, a Graduate Research Assistant (GRA). He provided us access to the participant’s answers to survey questions. As more and more surveys are filled out by users the team will continue to refine their algorithm to accommodate that extra data. As a further step we will work towards a hybrid recommender which will incorporate not only content, but also collaborative-based recommending.en_US
dc.description.sponsorshipNIH Grant 1R01DA039456-01en_US
dc.description.sponsorshipPrashant Chandrasekar
dc.language.isoen_USen_US
dc.subjectFriendicaen_US
dc.subjectSIRecommenderen_US
dc.subjectSimilarity Matrixen_US
dc.subjectHomophilyen_US
dc.subjectSocial Interactomeen_US
dc.subjectrecommenderen_US
dc.subjectAddiction Recovery Research Centeren_US
dc.titleSocial Interactome Recommender Projecten_US
dc.typePresentationen_US
dc.typeSoftwareen_US
dc.typeReporten_US


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